Early stage clinical trials of deep brain stimulation (DBS) of the subcallosal cingulate (SCC) region has demonstrated real potential to improve the lives of patients with treatment-resistant depression (TRD). However, the neurophysiological basis for TRD symptoms remains unknown and definition of electrophysiological biomarkers that could someday be useful in closed-loop DBS control systems remain to be defined. The goal of this project is to identify the key electrophysiological features of chronically recorded local field potentials (LFPs) in the SCC. We propose that recent advances in patient-specific modeling, coupled with novel clinical DBS devices that enable ongoing LFP recording, represent a unique opportunity to augment our understanding of SCC electrophysiology and TRD. Therefore, we will develop detailed computer models that simulate the SCC LFP and use them to help interpret the clinical longterm recordings acquired from TRD patients. We hypothesize that modulation of theta-band activity in SCC can be correlated with TRD symptom relief from DBS, and these LFP signals arise from the interaction of inhibitory and excitatory inputs on SCC pyramidal neurons. We will use patient-specific models of SCC LFPs to evaluate our hypotheses. The LFP models for this project will consist of volume conductor models of the DBS electrodes implanted in each patient's brain, coupled to biophysical models SCC pyramidal neurons generating the sources and sinks responsible for the experimentally recorded signals. We will analyze 10 patients enrolled in an investigator initiated clinical trial of SCC DBS for TRD (FDA IDE G130107). That trial will use the new Medtronic Activa PC+S experimental DBS system, which enables recording and telemetry of LFP signals from the implanted device. LFP measures of SCC oscillatory activity will also be accompanied by simultaneous acquisition of mood and clinical depression outcome measures. This unique collaborative research opportunity will integrate two DBS world experts, uniquely blending their specific skills and strengths, and apply cutting edge modeling methods to address real life clinical questions. The results of the project will expand our basic understanding of LFP signals in the human brain, and facilitate the evolution of closed-loop DBS technology for the treatment of depression. BROADER IMPACTS: This proposal takes advantage of an evolving paradigm shift in how depression is defined and treated. The concept that depression is a neurological disorder with a quantifiable neurophysiological signature (even though we do not yet know the exact details), may be accepted by learned scholars. However, the world at large is still lacking in basic education and elucidation on one of the most common afflictions in society. Specifically the work proposed in this project has great potential to provide a cellular-level understanding of mood regulatory circuits in human patients. This has important translational implications for future quantitative classification of mood disorders and brain-based criteria for recovery. Such paradigm shifts in the clinical documentation and classification of depression could represent a springboard for public education and enlightenment on depression, driven by scientific discovery. Dr. Mayberg is especially well positioned to facilitate this broader impact goal; however, the scientific data must be assembled. This 2-center collaboration will facilitate that process and provide a unique training opportunity for both graduate students and post-doctoral fellows in both computational neuroscience and systems neuroscience. This project will further provide important infrastructure for training the next generation of interdisciplinary team scientists that will be necessary to address the complex neuro-engineering demands of the burgeoning field of clinical neuromodulation.